Publicly and privately developed pharmacological data is readily available from both reference data and source data. Statistical information has been collected for many years on adverse reactions to drugs, including information on prescriptions, nutraceuticals, and over-the-counter medications. With this information, databases have been created that provide both reporting and data analysis of adverse drug reactions. Typically, this data is provided in a format that is not amenable to searching, such as in ASCII format.
Additionally, these databases are often in different structures and language formats, decreasing the efficiency and impeding effective use. Further, the variations in terminology and software languages employed by these disparate databases complicates conventional queries, making the results unreliable.
Various methods and techniques have been developed to address the need to provide ready access to pharmacological data and adverse events. However, none of these partial solutions, such as the Freedom of Information data provided by the FDA (which relies on “flat files”) or standard dictionaries such as the Medical Dictionary for Regulatory Activities (MedDRA™), have been integrated to allow consistent analysis and results.
Pharmacogenetics is the study of individual response to drugs as a function of genetic differences. These responses relate to how a drug functions in any given individual, how it is metabolized, its toxicity and dosage requirements. With the human genome project, pharmacogenetics has expanded into pharmacogenomics. Pharmacogenomics goes beyond pharmacogenetics, with the potential to find uses from drug discovery and development, target discovery and validation, and clinical trials; and to get that information into the doctor's office so that the right medicine is given to the right patient at the right time.
For pharmacogenomics to be effective, markers are needed that are indicative of the connection between drug response and genetic makeup. One such marker that is being diligently pursued is the single-nucleotide polymorphism (SNP). Databases are presently available that furnish a map of over a million SNPs. From this data, information has been collected regarding the allelic frequency of a SNP within an ethnically diverse population. There are also other databases which are more narrowly tailored and focus only on particular groups of SNPs such as those that code for proteins; provide a data set related to ADME (absorption, distribution, metabolism and excretion) genes and SNPs that are associated with how the body responds to drugs. Instead of single SNPs, some databases focus on SNPs that are found in haplotypes, which work together to cause a particular drug response.
Pharmacogenomics is being applied to pharmacodynamics, how a drug affects a disease. Additionally, pharmacogenomics is being applied to pharmacokinetics, or how the body processes a drug. While the pathway for drug intervention is usually well known, there are two important mechanisms to consider in pharmacokinetics—the pathway that metabolizes the drug itself, and other pathways that drugs and their metabolites may inadvertently and adversely affect. It is in these two areas that drug safety comes into play. In the first, there are distinctions among human genotypes in the ability to metabolize drugs. If a drug is not metabolized as predicted in the clinical trials, it could potentially build up to toxic levels. Different segments of the population metabolize drugs differently, providing a variety of potential reactions to a drug which can impact the dosage, safety, and efficacy of that drug and its usefulness for an individual patient.
A drug and its metabolites may affect other pathways for varying genotypes (or phenotypes). Currently available data on drug safety, as collected by regulators around the world, does not address genetic variances, although hundreds of different reactions are reported to occur in many body systems.
Accordingly, what is needed is an understanding of the impact of differing rates of metabolism on adverse drug events. Additionally, there is a need for a database providing drug safety data as collected by regulators around the world, particularly from a genetic perspective. Further, there is a need for a relational database that can assimilate and correlate these two sets of data, particularly from a genomic perspective.